According to an increase in types of television (TV) channels and programs, wide selections are provided to a user. On the other hand, inconvenience that the user needs to find a desired TV channel and program also occurs. As a method for solving the above inconvenience, proposed is a solution that utilizes a Bayesian network theory. The above method performs learning of a user TV watching pattern from a perspective of a probability and recommends a favorite TV channel/program for each user based on a learned probability value.
However, an existing approach method utilizing the Bayesian network theory has the following problems.
The original purpose of a TV is to provide relaxation. However, for example, in the case of intensively watching an Educational Broadcasting (EBS) channel to preparing for a college entrance examination, when a user later desires to watch other TV channels for the purpose of relaxation, the EBS channel acquires a high watching probability simply due to intensive viewing, which is different from the original intent of the user. On the contrary, when the user makes time out of busy schedule and watches a favorite TV program only for a short time, a watching probability is low and thus, the favorite TV program may not be considered within the recommending rankings of TV programs. Consequently, an optimal recommendation suitable for the intent of the user may not be performed.